1. What is Sample Mean Difference?

The Sample Mean Difference (SMD) is a standardized measure of effect size that indicates the difference between two means in terms of their standard deviations. It's commonly used in meta-analyses and other statistical comparisons.

2. How Does the Calculator Work?

The calculator uses the SMD formula:

Where:

• \( Mean1 \) — Mean of first sample
• \( Mean2 \) — Mean of second sample
• \( SD1 \) — Standard deviation of first sample
• \( SD2 \) — Standard deviation of second sample

Explanation: The numerator represents the raw difference between means, while the denominator standardizes this difference by the pooled standard deviation.

3. Importance of SMD Calculation

Details: SMD allows comparison of effect sizes across studies with different measurement scales. It's particularly valuable in meta-analyses where different studies may have used different measurement units.

4. Using the Calculator

Tips: Enter both means and standard deviations. The units must be consistent (all in the same measurement scale). Standard deviations must be non-negative.

5. Frequently Asked Questions (FAQ)

Q1: What does the SMD value mean?
A: An SMD of 0 means no difference. Values around 0.2 are considered small effects, 0.5 medium, and 0.8 large effects.

Q2: How does SMD differ from Cohen's d?
A: This is actually Cohen's d formula. The terms are often used interchangeably, though some variations exist.

Q3: When should I use SMD?
A: Use SMD when comparing means from different studies or when the measurement scales differ between groups.

Q4: What if my standard deviation is zero?
A: The calculation becomes undefined (division by zero) if both SDs are zero, indicating no variability in measurements.

Q5: Can SMD be negative?
A: Yes, negative values indicate the second mean is larger than the first. The absolute value represents the effect size magnitude.